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Objective:To explore the characteristics of T lymphocyte subsets and cytokines in hyperlipidemia-induced acute pancreatitis (HLAP) and its prognostic value.Methods:This study included 184 patients with acute pancreatitis (AP) admitted to the First Affiliated Hospital of Xiamen University from January 2018 to May 2021. Based on disease etiology, there were 92 HLAP cases and 92 non-hyperlipidemia-induced AP (NHLAP) cases. Stratified by disease severity according to 2012 Atlanta classification criteria, the patients were divided into the severe subgroup (SAP) and non-severe subgroup (NSAP). Peripheral venous blood samples were taken from all patients on day 1, 3, and 5 after admission. T lymphocyte subsets were determined by flow cytometry, and cytokines were detected by flow fluorometry. The number of CD4 +% and CD8 +% and the expression of cytokines were compared by Student’s t test or Mann-Whitney U analysis. Logistic regression analyses were performed to identify risk factors for severe AP, and a receiver operating characteristic (ROC) curve was constructed to predict severe AP. Statistical significance was taken as P<0.05. Results:Compared with the NHLAP group, patients in the HLAP group had lower CD4 +%, while higher levels of IL-2 on day 1 ( P<0.05), and had also lower CD4 +%, while higher levels of IL-4, IL-6, and IL-10 on day 3 ( P<0.05). Furthermore, IL-6 and IL-10 levels of the HLAP group were significantly increased compared to the NHLAP group on day 5 ( P<0.05). IL-10 levels in the SAP subgroup were significantly higher than those in the NSAP subgroup on day 1 ( P<0.05). Compared with the NSAP subgroup, the SAP subgroup had elevated levels of IL-2, IL-4, IL-6, IL-10 and IFN-γ on day 3 (all P<0.05), and had lower CD4 +%, while increased levels of IL-6 and IL-10 on day 5 (all P<0.05). Multivariate Logistic regression analysis showed that IL-10 was an immune indicator of independent risk factor for severe AP in the HLAP group on day 1 ( OR=1.139, 95% CI: 1.038-1.251, P<0.05). Finally, ROC analysis showed that the area under the curve of IL-10 to assess HLAP with severe AP was 0.772, and the best cut-off value for predicting severe AP was 5.6 pg/mL, with a sensitivity of 83.3% and a specificity of 68.8%. Conclusions:Changes of CD4 +% and cytokines are different between the HLAP and NHLAP groups. IL-10 can be used as a predictor of early disease severity in patients with HLAP.
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Objective:To establish an early prediction model with multiple indicators to predict the risk of severe acute pancreatitis (SAP) in hyperlipidemic acute pancreatitis (HLAP).Methods:The clinical data of 92 patients with HLAP admitted to the Emergency Department of our hospital from March 2018 to February 2020 were analyzed retrospectively. Among them, 29 cases deteriorated to SAP and 63 cases did not. Univariate analysis was used to screen predictive indicators related to hyperlipidemic severe acute pancreatitis (HL-SAP), and logistic regression analysis was used to screen independent predictive indicators related to HL-SAP. Then a prediction model was established. The area under (AUC) the receiver operating curve (ROC) was used to evaluate the predictive ability of each predictive indicator and the model for HL-SAP. Bootstrap resampling technology was used to validate the predictive ability of the model.Results:Univariate analysis showed that procalcitonin, D-dimer, C-reactive protein, albumin, cholesterol and CT grade had influence on the progression of HLAP to SAP ( P<0.05). Logistic regression analysis showed that D-dimer ( OR=2.112, 95% CI: 1.022-4.366; P<0.05), CT grade ( OR=5.818, 95% CI: 2.481-13.643; P<0.01) and cholesterol ( OR=1.146, 95% CI: 1.004-1.308; P<0.05) were independent risk factor of HL-SAP. The AUC of D-dimer, CT grade, cholesterol and the model were 0.802, 0.875, 0.665 and 0.927, respectively. Internal validation of the predictive ability of the model showed that the C-index was 0.927. Conclusions:In the early phase, application of the prediction model that composes D-dimer, CT grade and cholesterol has a good predictive effect on HL-SAP.
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<p><b>OBJECTIVE</b>To assess the differences in the health promotion demand of practitioners between urban and rural private enterprises by a comparative analysis, and to probe into the more scientific and targeted health promotion measures.</p><p><b>METHODS</b>Stratified cluster random sampling and self-designed questionnaire were adopted to survey 852 practitioners in urban and rural private enterprises of a Chinese city.</p><p><b>RESULTS</b>There were significant differences in practitioners between the two sorts of enterprises in terms of age, length of service, educational level, and forms of employment (P < 0.05 or P < 0.01). The basic knowledge and skills of practitioners in rural private enterprises were worse than those in urban private enterprises(P < 0.05 or P < 0.01). Practitioners in rural private enterprises were significantly less inclined to gain basic health promotion knowledge through enterprise training and network(P < 0.01). The demand of practitioners for health examination and hazard notification was significantly lower in rural private enterprises than in urban private enterprises (P < 0.05).</p><p><b>CONCLUSION</b>Focused and targeted health promotion should be carried out based on different demand characteristics of practitioners in rural and urban private enterprises.</p>